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Modeling and forecasting realized volatility: getting the most out of the jump component

Author

Listed:
  • Adam E Clements

    (QUT)

  • Yin Liao

    (QUT)

Abstract

Modeling and forecasting realized volatility is of paramount importance. Recent econometric developments allow total volatility to be decomposed into its' constituent continuous and jump components. While previous studies have examined the role of both components in forecasting, little analysis has been undertaken into how best to harness the jump component. This paper considers how to get the most out of the jump component for the purposes of forecasting total volatility. In combination with the magnitude of past jumps, the intensity of jump occurrence is examined. Estimated jump intensity from a point process model is used within a forecasting regression framework. Even in the presence of the diffusive part of total volatility, and past jump size, intensity is found to significantly improve forecast accuracy. The improvement is particularly apparent on the days when jumps occur or when markets are turbulent. Overall, the best way to harness the jump component for volatility forecasting is to make use of both the magnitude and probability of jump occurrences.

Suggested Citation

  • Adam E Clements & Yin Liao, 2013. "Modeling and forecasting realized volatility: getting the most out of the jump component," NCER Working Paper Series 93, National Centre for Econometric Research.
    • Handle: RePEc:qut:auncer:2013_5
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    File URL: http://www.ncer.edu.au/papers/documents/WP93.pdf
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    References listed on IDEAS

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    Cited by:

    1. Arnerić Josip & Poklepović Tea & Teai Juin Wen, 2018. "Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data," Business Systems Research, Sciendo, vol. 9(2), pages 18-34, July.

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    More about this item

    Keywords

    Realized volatility; diffusion; jumps; point process; Hawkes process; forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G00 - Financial Economics - - General - - - General

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